What is the most computationally efficient genetic algorithm?
First of all, for a lot of realistic problems, the fitness function evaluation is usually orders of magnitude greater in complexity than the rest of the genetic algorithm. This is not always true, but often is true (e.g. imagine trying to optimise a simulation where you need to execute the simulation completely to obtain the fitness). So optimising the GA itself might only be helpful when the fitness function is lightweight (e.g. it's a mathematical function as used in some competitions).
While your diagram shows a lot of operators, it doesn't show variations of the GA itself. Here are two:

generational: selects a new population at each iteration after performing mutation and crossover on the existing population to generate offspring (which are usually more in number than the population size)
steady state: maintains a single population and replaces only individuals during each iteration with their offspring according to selection/replacement rules, so uses less memory than a traditional GA

If you really wanted to maximise efficiency in the GA, a steady state approach would probably work best combined with tournament selection, as tournament selection does not require any sorting. The mutation/crossover operators you list are likely to all be quite efficient, but simpler methods are likely to to be the most efficient (e.g. bit flip mutation+1 point crossover). For a realistic problem, however, problem-specific operators usually work best.